Performance of the Nottingham hip fracture score (NHFS) as a predictor of 30-day mortality after proximal femur fracture in an older people Brazilian cohort
Bibliographic record
Abstract
Perioperative risk assessment helps inform clinical practice for older people with hip fractures. This is a cohort study, where perioperative risk screening, including NHFS, was performed at admission, followed by an evaluation of 30-day outcomes. 503 patients were included, 73% female, 79.4 ± 9.3 years old; 58% presented extracapsular and 42% intracapsular fractures, with a 30-day mortality of 9%. The NHFS was higher in the patients who died at 5.6 ± 1.1 compared to survivals at 4.3 ± 1.5 (p-value < 0.001). NHFS > 4 was associated with 30-day mortality observed by Cox regression adjusted by fracture type: HR 4.55 (95% CI 2.10-9.82) (p-value < 0.001) and Kaplan-Meyer Curve (HR 3.94; 95% CI 2.19-7.07; p-value < 0.001). ROC curve showed the accuracy of NHFS in explaining 30-day mortality (AUC 0.74; 95% CI 0.67-0.81). Complications were higher among patients with NHFS > 4. The performance of NHFS was better than the traditional perioperative risk ASA score. Therefore, NHFS can be implemented in real-world clinical practice to estimate the 30-day mortality risk for hip fracture in older patients in Brazil. NHFS > 4 is critical for 30-day mortality and complications; this cutoff helps inform clinical practice. The present study might motivate other centers to consider NHFS in their perioperative risk assessment routine.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".